Overview

Dataset statistics

Number of variables19
Number of observations9821
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory156.0 B

Variable types

Categorical7
Text4
Numeric7
DateTime1

Alerts

VoteCount is highly overall correlated with Budget and 1 other fieldsHigh correlation
Budget is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
Revenue is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
OriginalLanguage is highly overall correlated with North America and 1 other fieldsHigh correlation
North America is highly overall correlated with OriginalLanguageHigh correlation
Asia is highly overall correlated with OriginalLanguageHigh correlation
Oceania is highly imbalanced (84.3%)Imbalance
South America is highly imbalanced (90.7%)Imbalance
Africa is highly imbalanced (93.8%)Imbalance
Popularity is highly skewed (γ1 = 20.02850245)Skewed
VoteAverage has 184 (1.9%) zerosZeros
VoteCount has 183 (1.9%) zerosZeros
Budget has 4335 (44.1%) zerosZeros
Revenue has 4015 (40.9%) zerosZeros

Reproduction

Analysis started2023-11-07 19:33:14.196984
Analysis finished2023-11-07 19:33:23.527125
Duration9.33 seconds
Software versionydata-profiling vv4.5.0
Download configurationconfig.json

Variables

OriginalLanguage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
1
7398 
0
2423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%

Length

2023-11-07T14:33:23.779302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:23.903032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%

Most occurring characters

ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7398
75.3%
0 2423
 
24.7%
Distinct9818
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:24.158826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length709
Median length474
Mean length193.26209
Min length6

Characters and Unicode

Total characters1898027
Distinct characters92
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9815 ?
Unique (%)99.9%

Sample

1st rowarmed every weapon get hands skills use expendables world ’ last line defense team gets called options table new team members new styles tactics going give “ new blood ” whole new meaning
2nd rowrobert mccall finds home southern italy discovers friends control local crime bosses events turn deadly mccall knows become friends protector taking mafia
3rd row1980s hollywood action star johnny cage looking become alist actor costar jennifer goes missing set johnny finds thrust world filled shadows danger deceit embarks bloody journey johnny quickly discovers city angels devils midst
4th rowethan hunt imf team embark dangerous mission yet track terrifying new weapon threatens humanity falls wrong hands control future worlds fate stake dark forces ethans past closing deadly race around globe begins confronted mysterious allpowerful enemy ethan must consider nothing matter mission—not even lives cares
5th rowyoung pregnant woman named mia escapes country war hiding maritime container aboard cargo ship violent storm mia gives birth child lost sea must fight survive
ValueCountFrequency (%)
life 1370
 
0.5%
new 1332
 
0.5%
one 1280
 
0.5%
young 1277
 
0.5%
world 1126
 
0.4%
1034
 
0.4%
must 1021
 
0.4%
family 956
 
0.4%
two 917
 
0.3%
find 888
 
0.3%
Other values (31434) 253743
95.8%
2023-11-07T14:33:24.736383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
255123
13.4%
e 204546
 
10.8%
a 128308
 
6.8%
s 124857
 
6.6%
r 123346
 
6.5%
i 120632
 
6.4%
n 118239
 
6.2%
t 110706
 
5.8%
o 104645
 
5.5%
l 84725
 
4.5%
Other values (82) 522900
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1638652
86.3%
Space Separator 255123
 
13.4%
Decimal Number 1928
 
0.1%
Final Punctuation 1154
 
0.1%
Dash Punctuation 876
 
< 0.1%
Initial Punctuation 155
 
< 0.1%
Other Punctuation 112
 
< 0.1%
Other Symbol 12
 
< 0.1%
Format 4
 
< 0.1%
Modifier Letter 3
 
< 0.1%
Other values (4) 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 204546
12.5%
a 128308
 
7.8%
s 124857
 
7.6%
r 123346
 
7.5%
i 120632
 
7.4%
n 118239
 
7.2%
t 110706
 
6.8%
o 104645
 
6.4%
l 84725
 
5.2%
d 64266
 
3.9%
Other values (49) 454382
27.7%
Decimal Number
ValueCountFrequency (%)
1 540
28.0%
0 408
21.2%
9 258
13.4%
2 166
 
8.6%
6 105
 
5.4%
8 100
 
5.2%
7 100
 
5.2%
3 99
 
5.1%
5 82
 
4.3%
4 70
 
3.6%
Final Punctuation
ValueCountFrequency (%)
1034
89.6%
118
 
10.2%
» 2
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
117
75.5%
36
 
23.2%
« 2
 
1.3%
Format
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Dash Punctuation
ValueCountFrequency (%)
587
67.0%
289
33.0%
Other Punctuation
ValueCountFrequency (%)
109
97.3%
3
 
2.7%
Other Symbol
ValueCountFrequency (%)
10
83.3%
® 2
 
16.7%
Nonspacing Mark
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
Other Number
ValueCountFrequency (%)
¹ 1
50.0%
² 1
50.0%
Space Separator
ValueCountFrequency (%)
255123
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1638652
86.3%
Common 259372
 
13.7%
Inherited 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 204546
12.5%
a 128308
 
7.8%
s 124857
 
7.6%
r 123346
 
7.5%
i 120632
 
7.4%
n 118239
 
7.2%
t 110706
 
6.8%
o 104645
 
6.4%
l 84725
 
5.2%
d 64266
 
3.9%
Other values (49) 454382
27.7%
Common
ValueCountFrequency (%)
255123
98.4%
1034
 
0.4%
587
 
0.2%
1 540
 
0.2%
0 408
 
0.2%
289
 
0.1%
9 258
 
0.1%
2 166
 
0.1%
118
 
< 0.1%
117
 
< 0.1%
Other values (21) 732
 
0.3%
Inherited
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1895301
99.9%
Punctuation 2297
 
0.1%
None 412
 
< 0.1%
Letterlike Symbols 10
 
< 0.1%
Modifier Letters 3
 
< 0.1%
Diacriticals 3
 
< 0.1%
Alphabetic PF 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
255123
13.5%
e 204546
 
10.8%
a 128308
 
6.8%
s 124857
 
6.6%
r 123346
 
6.5%
i 120632
 
6.4%
n 118239
 
6.2%
t 110706
 
5.8%
o 104645
 
5.5%
l 84725
 
4.5%
Other values (27) 520174
27.4%
Punctuation
ValueCountFrequency (%)
1034
45.0%
587
25.6%
289
 
12.6%
118
 
5.1%
117
 
5.1%
109
 
4.7%
36
 
1.6%
3
 
0.1%
2
 
0.1%
1
 
< 0.1%
None
ValueCountFrequency (%)
é 205
49.8%
á 26
 
6.3%
ō 25
 
6.1%
í 24
 
5.8%
è 16
 
3.9%
ū 11
 
2.7%
ï 11
 
2.7%
ä 9
 
2.2%
ç 9
 
2.2%
ñ 8
 
1.9%
Other values (29) 68
 
16.5%
Letterlike Symbols
ValueCountFrequency (%)
10
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 3
100.0%
Diacriticals
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%

Popularity
Real number (ℝ)

SKEWED 

Distinct7955
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.389345
Minimum13.049
Maximum3741.062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:24.931565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13.049
5-th percentile13.472
Q115.583
median20.092
Q330.488
95-th percentile80.022
Maximum3741.062
Range3728.013
Interquartile range (IQR)14.905

Descriptive statistics

Standard deviation85.031911
Coefficient of variation (CV)2.4726238
Kurtosis594.5303
Mean34.389345
Median Absolute Deviation (MAD)5.446
Skewness20.028502
Sum337737.76
Variance7230.4259
MonotonicityDecreasing
2023-11-07T14:33:25.109694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.297 6
 
0.1%
15.499 6
 
0.1%
16.146 6
 
0.1%
13.572 5
 
0.1%
13.112 5
 
0.1%
13.67 5
 
0.1%
13.84 5
 
0.1%
14.739 5
 
0.1%
14.84 4
 
< 0.1%
13.135 4
 
< 0.1%
Other values (7945) 9770
99.5%
ValueCountFrequency (%)
13.049 3
< 0.1%
13.05 1
 
< 0.1%
13.051 3
< 0.1%
13.052 2
< 0.1%
13.054 3
< 0.1%
13.055 2
< 0.1%
13.056 1
 
< 0.1%
13.057 2
< 0.1%
13.059 1
 
< 0.1%
13.063 2
< 0.1%
ValueCountFrequency (%)
3741.062 1
< 0.1%
2471.515 1
< 0.1%
2223.43 1
< 0.1%
2032.927 1
< 0.1%
1627.678 1
< 0.1%
1594.559 1
< 0.1%
1521.075 1
< 0.1%
1469.177 1
< 0.1%
1315.518 1
< 0.1%
1304.978 1
< 0.1%
Distinct5908
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
Minimum1902-04-17 00:00:00
Maximum2027-05-05 00:00:00
2023-11-07T14:33:25.292460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:25.489505image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Title
Text

Distinct8941
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:25.864859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length78
Median length61
Mean length13.800733
Min length1

Characters and Unicode

Total characters135537
Distinct characters70
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8279 ?
Unique (%)84.3%

Sample

1st rowexpend4bles
2nd rowequalizer
3rd rowmortal kombat legends cage match
4th rowmission impossible dead reckoning part one
5th rownowhere
ValueCountFrequency (%)
movie 164
 
0.8%
love 117
 
0.5%
man 103
 
0.5%
ii 100
 
0.5%
last 90
 
0.4%
one 86
 
0.4%
dragon 77
 
0.4%
dead 77
 
0.4%
part 76
 
0.4%
night 74
 
0.3%
Other values (7358) 20498
95.5%
2023-11-07T14:33:26.484876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14014
 
10.3%
11641
 
8.6%
a 10878
 
8.0%
r 9554
 
7.0%
i 9140
 
6.7%
n 8733
 
6.4%
s 8683
 
6.4%
o 8580
 
6.3%
t 7673
 
5.7%
l 6611
 
4.9%
Other values (60) 40030
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 123720
91.3%
Space Separator 11641
 
8.6%
Decimal Number 139
 
0.1%
Dash Punctuation 14
 
< 0.1%
Other Punctuation 8
 
< 0.1%
Final Punctuation 4
 
< 0.1%
Other Number 4
 
< 0.1%
Currency Symbol 2
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%
Modifier Letter 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14014
11.3%
a 10878
 
8.8%
r 9554
 
7.7%
i 9140
 
7.4%
n 8733
 
7.1%
s 8683
 
7.0%
o 8580
 
6.9%
t 7673
 
6.2%
l 6611
 
5.3%
d 4934
 
4.0%
Other values (34) 34920
28.2%
Decimal Number
ValueCountFrequency (%)
3 40
28.8%
1 28
20.1%
2 18
12.9%
4 14
 
10.1%
9 10
 
7.2%
0 10
 
7.2%
7 7
 
5.0%
5 6
 
4.3%
8 4
 
2.9%
6 2
 
1.4%
Other Punctuation
ValueCountFrequency (%)
¡ 3
37.5%
¿ 3
37.5%
1
 
12.5%
· 1
 
12.5%
Other Number
ValueCountFrequency (%)
³ 2
50.0%
² 1
25.0%
1
25.0%
Dash Punctuation
ValueCountFrequency (%)
10
71.4%
4
 
28.6%
Space Separator
ValueCountFrequency (%)
11641
100.0%
Final Punctuation
ValueCountFrequency (%)
4
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̀ 2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123720
91.3%
Common 11815
 
8.7%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14014
11.3%
a 10878
 
8.8%
r 9554
 
7.7%
i 9140
 
7.4%
n 8733
 
7.1%
s 8683
 
7.0%
o 8580
 
6.9%
t 7673
 
6.2%
l 6611
 
5.3%
d 4934
 
4.0%
Other values (34) 34920
28.2%
Common
ValueCountFrequency (%)
11641
98.5%
3 40
 
0.3%
1 28
 
0.2%
2 18
 
0.2%
4 14
 
0.1%
10
 
0.1%
9 10
 
0.1%
0 10
 
0.1%
7 7
 
0.1%
5 6
 
0.1%
Other values (15) 31
 
0.3%
Inherited
ValueCountFrequency (%)
̀ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 135412
99.9%
None 101
 
0.1%
Punctuation 19
 
< 0.1%
Latin Ext Additional 2
 
< 0.1%
Diacriticals 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14014
 
10.3%
11641
 
8.6%
a 10878
 
8.0%
r 9554
 
7.1%
i 9140
 
6.7%
n 8733
 
6.4%
s 8683
 
6.4%
o 8580
 
6.3%
t 7673
 
5.7%
l 6611
 
4.9%
Other values (27) 39905
29.5%
None
ValueCountFrequency (%)
é 48
47.5%
á 7
 
6.9%
ó 5
 
5.0%
í 5
 
5.0%
ā 3
 
3.0%
è 3
 
3.0%
¡ 3
 
3.0%
¿ 3
 
3.0%
à 3
 
3.0%
¢ 2
 
2.0%
Other values (16) 19
 
18.8%
Punctuation
ValueCountFrequency (%)
10
52.6%
4
 
21.1%
4
 
21.1%
1
 
5.3%
Latin Ext Additional
ValueCountFrequency (%)
2
100.0%
Diacriticals
ValueCountFrequency (%)
̀ 2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

VoteAverage
Real number (ℝ)

ZEROS 

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4190103
Minimum0
Maximum10
Zeros184
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:26.696510image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q16
median6.6
Q37.2
95-th percentile7.9
Maximum10
Range10
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.2661077
Coefficient of variation (CV)0.19724344
Kurtosis10.689806
Mean6.4190103
Median Absolute Deviation (MAD)0.6
Skewness-2.5370247
Sum63041.1
Variance1.6030286
MonotonicityNot monotonic
2023-11-07T14:33:26.885790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 462
 
4.7%
6.8 443
 
4.5%
7 443
 
4.5%
6.6 434
 
4.4%
6.3 434
 
4.4%
6.7 426
 
4.3%
6.9 420
 
4.3%
6.2 410
 
4.2%
6.4 409
 
4.2%
6.1 386
 
3.9%
Other values (63) 5554
56.6%
ValueCountFrequency (%)
0 184
1.9%
1 10
 
0.1%
1.5 1
 
< 0.1%
2 4
 
< 0.1%
2.3 1
 
< 0.1%
2.5 1
 
< 0.1%
2.7 2
 
< 0.1%
2.8 1
 
< 0.1%
2.9 3
 
< 0.1%
3 10
 
0.1%
ValueCountFrequency (%)
10 10
0.1%
9.8 1
 
< 0.1%
9.5 2
 
< 0.1%
9.3 1
 
< 0.1%
9 8
 
0.1%
8.9 1
 
< 0.1%
8.8 5
 
0.1%
8.7 2
 
< 0.1%
8.6 6
 
0.1%
8.5 20
0.2%

VoteCount
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3524
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1632.7309
Minimum0
Maximum34628
Zeros183
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:27.063309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1176
median573
Q31681
95-th percentile6942
Maximum34628
Range34628
Interquartile range (IQR)1505

Descriptive statistics

Standard deviation2960.6589
Coefficient of variation (CV)1.8133172
Kurtosis21.975762
Mean1632.7309
Median Absolute Deviation (MAD)490
Skewness4.0448429
Sum16035050
Variance8765501.4
MonotonicityNot monotonic
2023-11-07T14:33:27.250842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183
 
1.9%
1 63
 
0.6%
2 53
 
0.5%
3 44
 
0.4%
5 44
 
0.4%
4 41
 
0.4%
8 37
 
0.4%
7 34
 
0.3%
10 30
 
0.3%
6 30
 
0.3%
Other values (3514) 9262
94.3%
ValueCountFrequency (%)
0 183
1.9%
1 63
 
0.6%
2 53
 
0.5%
3 44
 
0.4%
4 41
 
0.4%
5 44
 
0.4%
6 30
 
0.3%
7 34
 
0.3%
8 37
 
0.4%
9 27
 
0.3%
ValueCountFrequency (%)
34628 1
< 0.1%
32726 1
< 0.1%
30768 1
< 0.1%
29904 1
< 0.1%
29241 1
< 0.1%
28971 1
< 0.1%
27827 1
< 0.1%
27366 1
< 0.1%
26721 1
< 0.1%
26016 1
< 0.1%

Budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct707
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20682804
Minimum0
Maximum4.6 × 108
Zeros4335
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:27.437438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2500000
Q325000000
95-th percentile1 × 108
Maximum4.6 × 108
Range4.6 × 108
Interquartile range (IQR)25000000

Descriptive statistics

Standard deviation38965295
Coefficient of variation (CV)1.8839465
Kurtosis13.687833
Mean20682804
Median Absolute Deviation (MAD)2500000
Skewness3.219681
Sum2.0312582 × 1011
Variance1.5182942 × 1015
MonotonicityNot monotonic
2023-11-07T14:33:27.621835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4335
44.1%
20000000 208
 
2.1%
30000000 183
 
1.9%
25000000 171
 
1.7%
10000000 169
 
1.7%
15000000 164
 
1.7%
40000000 155
 
1.6%
5000000 143
 
1.5%
50000000 133
 
1.4%
35000000 129
 
1.3%
Other values (697) 4031
41.0%
ValueCountFrequency (%)
0 4335
44.1%
1 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
20 1
 
< 0.1%
26 1
 
< 0.1%
35 1
 
< 0.1%
ValueCountFrequency (%)
460000000 1
 
< 0.1%
379000000 1
 
< 0.1%
365000000 1
 
< 0.1%
356000000 1
 
< 0.1%
340000000 1
 
< 0.1%
300000000 4
< 0.1%
297000000 1
 
< 0.1%
294700000 1
 
< 0.1%
291000000 1
 
< 0.1%
274800000 1
 
< 0.1%
Distinct7330
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:27.914584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length148
Median length107
Mean length19.68659
Min length0

Characters and Unicode

Total characters193342
Distinct characters53
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7188 ?
Unique (%)73.2%

Sample

1st rowtheyll die theyre dead
2nd rowjustice knows borders
3rd rowneon lights suits shoulder pads jumping explosions slow motion
4th rowshare fate
5th rowattempting survive middle nowhere option
ValueCountFrequency (%)
one 502
 
1.6%
love 355
 
1.1%
story 295
 
1.0%
never 286
 
0.9%
world 244
 
0.8%
life 227
 
0.7%
time 200
 
0.6%
man 198
 
0.6%
back 193
 
0.6%
get 182
 
0.6%
Other values (5962) 28201
91.3%
2023-11-07T14:33:28.448295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24580
12.7%
23367
12.1%
r 12244
 
6.3%
t 12173
 
6.3%
n 11971
 
6.2%
a 11833
 
6.1%
s 11694
 
6.0%
o 11333
 
5.9%
i 11088
 
5.7%
l 8557
 
4.4%
Other values (43) 54502
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 169682
87.8%
Space Separator 23367
 
12.1%
Final Punctuation 113
 
0.1%
Other Punctuation 100
 
0.1%
Decimal Number 64
 
< 0.1%
Dash Punctuation 12
 
< 0.1%
Initial Punctuation 3
 
< 0.1%
Modifier Letter 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24580
14.5%
r 12244
 
7.2%
t 12173
 
7.2%
n 11971
 
7.1%
a 11833
 
7.0%
s 11694
 
6.9%
o 11333
 
6.7%
i 11088
 
6.5%
l 8557
 
5.0%
d 6827
 
4.0%
Other values (24) 47382
27.9%
Decimal Number
ValueCountFrequency (%)
1 13
20.3%
3 12
18.8%
2 11
17.2%
0 7
10.9%
9 5
 
7.8%
6 5
 
7.8%
5 4
 
6.2%
8 3
 
4.7%
4 2
 
3.1%
7 2
 
3.1%
Final Punctuation
ValueCountFrequency (%)
111
98.2%
2
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
8
66.7%
4
33.3%
Initial Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
23367
100.0%
Other Punctuation
ValueCountFrequency (%)
100
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 169682
87.8%
Common 23660
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24580
14.5%
r 12244
 
7.2%
t 12173
 
7.2%
n 11971
 
7.1%
a 11833
 
7.0%
s 11694
 
6.9%
o 11333
 
6.7%
i 11088
 
6.5%
l 8557
 
5.0%
d 6827
 
4.0%
Other values (24) 47382
27.9%
Common
ValueCountFrequency (%)
23367
98.8%
111
 
0.5%
100
 
0.4%
1 13
 
0.1%
3 12
 
0.1%
2 11
 
< 0.1%
8
 
< 0.1%
0 7
 
< 0.1%
9 5
 
< 0.1%
6 5
 
< 0.1%
Other values (9) 21
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 193099
99.9%
Punctuation 228
 
0.1%
None 14
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 24580
12.7%
23367
12.1%
r 12244
 
6.3%
t 12173
 
6.3%
n 11971
 
6.2%
a 11833
 
6.1%
s 11694
 
6.1%
o 11333
 
5.9%
i 11088
 
5.7%
l 8557
 
4.4%
Other values (27) 54259
28.1%
Punctuation
ValueCountFrequency (%)
111
48.7%
100
43.9%
8
 
3.5%
4
 
1.8%
2
 
0.9%
2
 
0.9%
1
 
0.4%
None
ValueCountFrequency (%)
é 4
28.6%
ü 2
14.3%
á 2
14.3%
ñ 2
14.3%
í 1
 
7.1%
ō 1
 
7.1%
ù 1
 
7.1%
ê 1
 
7.1%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

RunTime
Real number (ℝ)

Distinct219
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.60574
Minimum0
Maximum400
Zeros77
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:28.647563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q191
median101
Q3115
95-th percentile141
Maximum400
Range400
Interquartile range (IQR)24

Descriptive statistics

Standard deviation26.178903
Coefficient of variation (CV)0.25514072
Kurtosis6.7184279
Mean102.60574
Median Absolute Deviation (MAD)12
Skewness-0.18260548
Sum1007691
Variance685.33497
MonotonicityNot monotonic
2023-11-07T14:33:28.842461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 291
 
3.0%
95 282
 
2.9%
100 271
 
2.8%
93 265
 
2.7%
105 244
 
2.5%
97 239
 
2.4%
98 237
 
2.4%
94 225
 
2.3%
92 225
 
2.3%
101 225
 
2.3%
Other values (209) 7317
74.5%
ValueCountFrequency (%)
0 77
0.8%
2 2
 
< 0.1%
3 7
 
0.1%
4 6
 
0.1%
5 8
 
0.1%
6 14
 
0.1%
7 9
 
0.1%
8 5
 
0.1%
9 5
 
0.1%
10 11
 
0.1%
ValueCountFrequency (%)
400 1
< 0.1%
333 1
< 0.1%
254 1
< 0.1%
248 1
< 0.1%
247 1
< 0.1%
242 2
< 0.1%
240 1
< 0.1%
238 2
< 0.1%
237 1
< 0.1%
230 1
< 0.1%

Revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5544
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62888316
Minimum0
Maximum2.923706 × 109
Zeros4015
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:29.030292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4100000
Q355181129
95-th percentile3.0997999 × 108
Maximum2.923706 × 109
Range2.923706 × 109
Interquartile range (IQR)55181129

Descriptive statistics

Standard deviation1.5690945 × 108
Coefficient of variation (CV)2.4950494
Kurtosis55.200124
Mean62888316
Median Absolute Deviation (MAD)4100000
Skewness5.8884203
Sum6.1762615 × 1011
Variance2.4620577 × 1016
MonotonicityNot monotonic
2023-11-07T14:33:29.214711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4015
40.9%
11000000 11
 
0.1%
10000000 10
 
0.1%
12000000 10
 
0.1%
2000000 10
 
0.1%
30000000 9
 
0.1%
25000000 8
 
0.1%
7000000 8
 
0.1%
8000000 7
 
0.1%
5000000 7
 
0.1%
Other values (5534) 5726
58.3%
ValueCountFrequency (%)
0 4015
40.9%
1 1
 
< 0.1%
3 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
29 1
 
< 0.1%
43 1
 
< 0.1%
94 1
 
< 0.1%
126 1
 
< 0.1%
201 1
 
< 0.1%
ValueCountFrequency (%)
2923706026 1
< 0.1%
2800000000 1
< 0.1%
2320250281 1
< 0.1%
2264162353 1
< 0.1%
2068223624 1
< 0.1%
2052415039 1
< 0.1%
1921847111 1
< 0.1%
1671537444 1
< 0.1%
1663075401 1
< 0.1%
1518815515 1
< 0.1%

Genres
Text

Distinct2269
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
2023-11-07T14:33:29.380965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length76
Median length56
Mean length19.847979
Min length3

Characters and Unicode

Total characters194927
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1414 ?
Unique (%)14.4%

Sample

1st rowaction adventure thriller
2nd rowaction thriller crime
3rd rowanimation action fantasy
4th rowaction thriller
5th rowthriller drama
ValueCountFrequency (%)
drama 3743
14.5%
comedy 2972
11.5%
action 2753
10.6%
thriller 2634
10.2%
adventure 1870
 
7.2%
romance 1553
 
6.0%
horror 1537
 
5.9%
fantasy 1332
 
5.1%
family 1330
 
5.1%
animation 1307
 
5.1%
Other values (9) 4839
18.7%
2023-11-07T14:33:29.773849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 20720
10.6%
r 20239
10.4%
e 16178
 
8.3%
16049
 
8.2%
i 15123
 
7.8%
m 13658
 
7.0%
o 13641
 
7.0%
t 12911
 
6.6%
n 12865
 
6.6%
c 12606
 
6.5%
Other values (9) 40937
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 178878
91.8%
Space Separator 16049
 
8.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 20720
11.6%
r 20239
11.3%
e 16178
9.0%
i 15123
8.5%
m 13658
7.6%
o 13641
7.6%
t 12911
7.2%
n 12865
 
7.2%
c 12606
 
7.0%
d 8735
 
4.9%
Other values (8) 32202
18.0%
Space Separator
ValueCountFrequency (%)
16049
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178878
91.8%
Common 16049
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 20720
11.6%
r 20239
11.3%
e 16178
9.0%
i 15123
8.5%
m 13658
7.6%
o 13641
7.6%
t 12911
7.2%
n 12865
 
7.2%
c 12606
 
7.0%
d 8735
 
4.9%
Other values (8) 32202
18.0%
Common
ValueCountFrequency (%)
16049
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 20720
10.6%
r 20239
10.4%
e 16178
 
8.3%
16049
 
8.2%
i 15123
 
7.8%
m 13658
 
7.0%
o 13641
 
7.0%
t 12911
 
6.6%
n 12865
 
6.6%
c 12606
 
6.5%
Other values (9) 40937
21.0%

North America
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
1
6940 
0
2881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Length

2023-11-07T14:33:29.943177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:30.055607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Most occurring characters

ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6940
70.7%
0 2881
29.3%

Europe
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
0
7131 
1
2690 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Length

2023-11-07T14:33:30.178011image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:30.289750image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Most occurring characters

ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7131
72.6%
1 2690
 
27.4%

Asia
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
0
8125 
1
1696 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Length

2023-11-07T14:33:30.413767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:30.525428image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8125
82.7%
1 1696
 
17.3%

Oceania
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
0
9596 
1
 
225

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

Length

2023-11-07T14:33:30.858336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:30.970397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9596
97.7%
1 225
 
2.3%

South America
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
0
9705 
1
 
116

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Length

2023-11-07T14:33:31.091482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:31.202328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9705
98.8%
1 116
 
1.2%

Africa
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size153.5 KiB
0
9750 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9821
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Length

2023-11-07T14:33:31.323901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T14:33:31.435820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9821
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 9821
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9750
99.3%
1 71
 
0.7%

Year
Real number (ℝ)

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.0433
Minimum1902
Maximum2027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size115.1 KiB
2023-11-07T14:33:31.576817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1902
5-th percentile1974
Q12000
median2012
Q32019
95-th percentile2023
Maximum2027
Range125
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.243302
Coefficient of variation (CV)0.00809315
Kurtosis2.9880936
Mean2007.0433
Median Absolute Deviation (MAD)9
Skewness-1.5916344
Sum19711172
Variance263.84487
MonotonicityNot monotonic
2023-11-07T14:33:31.769298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2023 799
 
8.1%
2022 666
 
6.8%
2021 456
 
4.6%
2018 443
 
4.5%
2019 436
 
4.4%
2017 391
 
4.0%
2020 383
 
3.9%
2016 341
 
3.5%
2015 309
 
3.1%
2014 303
 
3.1%
Other values (97) 5294
53.9%
ValueCountFrequency (%)
1902 1
 
< 0.1%
1910 1
 
< 0.1%
1920 1
 
< 0.1%
1921 1
 
< 0.1%
1922 1
 
< 0.1%
1925 2
< 0.1%
1927 3
< 0.1%
1928 1
 
< 0.1%
1929 1
 
< 0.1%
1930 1
 
< 0.1%
ValueCountFrequency (%)
2027 1
 
< 0.1%
2026 1
 
< 0.1%
2025 3
 
< 0.1%
2024 15
 
0.2%
2023 799
8.1%
2022 666
6.8%
2021 456
4.6%
2020 383
3.9%
2019 436
4.4%
2018 443
4.5%

Interactions

2023-11-07T14:33:22.136553image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:16.859072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.715340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.600084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.643625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.442316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.294478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.263002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:16.992415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.835268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.756995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.756624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.566534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.415006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.379716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.109877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.955702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.906743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.867569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.683242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.527389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.506809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.230487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.135338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.034151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.986187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.807532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.650160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.618137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.344424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.242827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.148946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.092732image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.922661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.763728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.744632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.471994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.367192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.276058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.213293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.049295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.891210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.865764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:17.592828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:18.483173image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:19.521796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:20.329319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:21.172235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T14:33:22.014470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T14:33:31.908735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
PopularityVoteAverageVoteCountBudgetRunTimeRevenueYearOriginalLanguageNorth AmericaEuropeAsiaOceaniaSouth AmericaAfrica
Popularity1.0000.1610.3470.2460.0760.2800.1810.0080.0110.0080.0200.0000.0000.000
VoteAverage0.1611.0000.3050.0480.3060.150-0.0900.1780.1590.0370.1400.0150.1230.022
VoteCount0.3470.3051.0000.6930.3470.744-0.2990.1900.2060.0210.0990.0410.0000.009
Budget0.2460.0480.6931.0000.3650.793-0.2590.2090.2300.0290.0790.0420.0160.000
RunTime0.0760.3060.3470.3651.0000.377-0.0990.1030.0910.1120.1320.0000.0000.000
Revenue0.2800.1500.7440.7930.3771.000-0.3130.1140.1300.0410.0550.0370.0000.000
Year0.181-0.090-0.299-0.259-0.099-0.3131.0000.1570.1890.1050.1100.0350.0540.015
OriginalLanguage0.0080.1780.1900.2090.1030.1140.1571.0000.7880.1300.5800.0750.1170.026
North America0.0110.1590.2060.2300.0910.1300.1890.7881.0000.2630.4960.0140.0870.032
Europe0.0080.0370.0210.0290.1120.0410.1050.1300.2631.0000.1820.0000.0000.025
Asia0.0200.1400.0990.0790.1320.0550.1100.5800.4960.1821.0000.0260.0270.019
Oceania0.0000.0150.0410.0420.0000.0370.0350.0750.0140.0000.0261.0000.0000.011
South America0.0000.1230.0000.0160.0000.0000.0540.1170.0870.0000.0270.0001.0000.000
Africa0.0000.0220.0090.0000.0000.0000.0150.0260.0320.0250.0190.0110.0001.000

Missing values

2023-11-07T14:33:23.051826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T14:33:23.370687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OriginalLanguageOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYear
Id
2990541armed every weapon get hands skills use expendables world ’ last line defense team gets called options table new team members new styles tactics going give “ new blood ” whole new meaning3741.0622023-09-15expend4bles6.4364100000000theyll die theyre dead10330000000action adventure thriller0000002023
9263931robert mccall finds home southern italy discovers friends control local crime bosses events turn deadly mccall knows become friends protector taking mafia2471.5152023-08-30equalizer7.3102770000000justice knows borders109176933602action thriller crime1000002023
103406211980s hollywood action star johnny cage looking become alist actor costar jennifer goes missing set johnny finds thrust world filled shadows danger deceit embarks bloody journey johnny quickly discovers city angels devils midst2223.4302023-10-17mortal kombat legends cage match7.8270neon lights suits shoulder pads jumping explosions slow motion800animation action fantasy1000002023
5752641ethan hunt imf team embark dangerous mission yet track terrifying new weapon threatens humanity falls wrong hands control future worlds fate stake dark forces ethans past closing deadly race around globe begins confronted mysterious allpowerful enemy ethan must consider nothing matter mission—not even lives cares2032.9272023-07-08mission impossible dead reckoning part one7.71799291000000share fate164567148955action thriller1000002023
11515340young pregnant woman named mia escapes country war hiding maritime container aboard cargo ship violent storm mia gives birth child lost sea must fight survive1627.6782023-09-29nowhere7.66860attempting survive middle nowhere option1090thriller drama0000002023
9680511france priest violently murdered sister irene begins investigate comes facetoface powerful evil1594.5592023-09-06nun ii7.0108638500000confess sins110262010000horror mystery thriller1000002023
9612680grieving loss best friend couldnt protect exbodyguard sets fulfill dear friends last wish sweet revenge1521.0752023-10-05ballerina7.02000merciless ruthless hell930action crime thriller0000002023
9514911events saw saw ii sick desperate john kramer travels mexico risky experimental medical procedure hopes miracle cure cancer discover entire operation scam defraud vulnerable armed newfound purpose infamous serial killer returns work turning tables con artists signature visceral way devious deranged ingenious traps1469.1772023-09-26saw x7.328713000000witness return jigsaw11871984243horror thriller1000002023
9804891ultimate wishfulfillment tale teenage gran turismo player whose gaming skills series nissan competitions become actual professional racecar driver1315.5182023-08-09gran turismo8.1112760000000gamer racer135114800000adventure action drama1000002023
9372491tech blogger lands interview tech guru stops attack finds mysterious ring takes back seconds past1304.9782023-09-29seconds5.41110rewind past avenge future990thriller sciencefiction action1000002023
OriginalLanguageOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYear
Id
110921rusty sabich deputy prosecutor engaged obsessive affair coworker murdered soon hes accused crime fight clear name becomes whirlpool lies hidden passions13.0541990-07-27presumed innocent6.859522000000people would kill love127221303188mystery crime thriller1000001990
6644231story pioneering project rehabilitate child survivors holocaust shores lake windermere13.0522020-01-27windermere children7.5960880drama tvmovie history0100002020
30771one sons late dr henry frankenstein finds fathers ghoulish creation coma revives find monster controlled ygor bent revenge13.0521939-01-13son frankenstein6.7205420000black shadows past bred halfman halfdemon creating new terrible juggernaut destruction990horror sciencefiction1000001939
4135430unconventional thinker helps budding cinematographer gain new perspective life13.0512016-11-23dear zindagi7.121043000001513376375drama romance0010002016
144000powerful billionaire murdered secret adoptive son must race prove legitimacy find fathers killers stop taking financial empire13.0512008-12-17heir apparent largo winch6.0486254127601080adventure drama action thriller0110002008
27491eastern european criminals oleg emil come new york city pick share heist score oleg steals video camera starts filming activities legal illegal learn american media circus make remorseless killer look like victim make rich target mediasavvy nypd homicide detective eddie flemming medianaive fdny fire marshal jordy warsaw cops investigating murder torching former criminal partner filming everything sell local tabloid tv show top story13.0512001-03-01minutes5.964660000000america likes watch12056359980action crime thriller1100002001
111281watchful eye mentor captain mike kennedy probationary firefighter jack morrison matures seasoned veteran baltimore fire station however jack reached crossroads sacrifices hes made put harms way innumerable times significantly impacted relationship wife kids13.0502004-10-01ladder6.470760000000greatest challenge lies rescuing one11574541707drama action thriller1000002004
484482040yearold bertrand suffering depression last two years barely able keep head water despite medication gulps day every day wifes encouragement unable find meaning life curiously end finding sense purpose swimming pool joining allmale synchronised swimming team13.0492018-10-24sink swim6.9139801220drama comedy0100002018
4537551man stranded arctic finally receive long awaited rescue however tragic accident opportunity lost must decide whether remain relative safety camp embark deadly trek unknown potential salvation13.0492018-11-21arctic6.510952000000survival option984100000drama1100002018
545181tells story justin bieber kid canada hair smile voice chronicles unprecedented rise fame way busking streets stratford canada putting videos youtube selling madison square garden new york headline act world tour features usher scooter braun ludacris sean kingston antonio la reid boyz ii men miley cyrus jaden smith justins family members parts crew huge fanbase mix interviews guest performances released 3d theaters around world highest grossing concert movie time beating previous record held michael jacksons13.0492011-02-11justin bieber never say never5.237813000000find whats possible never give10598500000music documentary family1000002011